Details
| Original language | English |
|---|---|
| Title of host publication | 2018 Annual American Control Conference, ACC 2018 |
| Pages | 728-734 |
| Number of pages | 7 |
| Publication status | Published - 9 Aug 2018 |
| Externally published | Yes |
| Event | 2018 Annual American Control Conference (ACC) - Milwaukee, WI Duration: 27 Jun 2018 → 29 Jun 2018 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| Volume | 2018-June |
| ISSN (Print) | 0743-1619 |
Abstract
In this paper, we present a novel constraint tightening approach for nonlinear robust model predictive control (MPC). This approach uses a simple constructive constraint tightening based on growing tubes. Contrary to other approaches, we require no complex offline computations to obtain a stabilizing control law. Instead, we consider the notion of incremental stabilizability and design tubes based on an estimate of the achievable exponential decay rate. In addition, we show how this tightening can be used as an ad-hoc modification to improve the robustness of MPC without terminal constraints. We study the system theoretic properties of the resulting closed-loop system, including bounds on the region of attraction and the minimal robust positively invariant (RPI) set. Within an MPC framework without terminal constraints, the proposed constraint tightening leads to a nonlinear robust controller without complex design procedures, which makes it appealing for practical applications.
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
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2018 Annual American Control Conference, ACC 2018. 2018. p. 728-734 8431892 (Proceedings of the American Control Conference; Vol. 2018-June).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - A novel constraint tightening approach for nonlinear robust model predictive control
AU - Köhler, Johannes
AU - Müller, Matthias A.
AU - Allgöwer, Frank
N1 - Funding information: The authors would like to thank the German Research Foundation (DFG) for financial support of the project within Soft Tissue Robotics (GRK 2198/1).
PY - 2018/8/9
Y1 - 2018/8/9
N2 - In this paper, we present a novel constraint tightening approach for nonlinear robust model predictive control (MPC). This approach uses a simple constructive constraint tightening based on growing tubes. Contrary to other approaches, we require no complex offline computations to obtain a stabilizing control law. Instead, we consider the notion of incremental stabilizability and design tubes based on an estimate of the achievable exponential decay rate. In addition, we show how this tightening can be used as an ad-hoc modification to improve the robustness of MPC without terminal constraints. We study the system theoretic properties of the resulting closed-loop system, including bounds on the region of attraction and the minimal robust positively invariant (RPI) set. Within an MPC framework without terminal constraints, the proposed constraint tightening leads to a nonlinear robust controller without complex design procedures, which makes it appealing for practical applications.
AB - In this paper, we present a novel constraint tightening approach for nonlinear robust model predictive control (MPC). This approach uses a simple constructive constraint tightening based on growing tubes. Contrary to other approaches, we require no complex offline computations to obtain a stabilizing control law. Instead, we consider the notion of incremental stabilizability and design tubes based on an estimate of the achievable exponential decay rate. In addition, we show how this tightening can be used as an ad-hoc modification to improve the robustness of MPC without terminal constraints. We study the system theoretic properties of the resulting closed-loop system, including bounds on the region of attraction and the minimal robust positively invariant (RPI) set. Within an MPC framework without terminal constraints, the proposed constraint tightening leads to a nonlinear robust controller without complex design procedures, which makes it appealing for practical applications.
UR - http://www.scopus.com/inward/record.url?scp=85052560531&partnerID=8YFLogxK
U2 - 10.23919/ACC.2018.8431892
DO - 10.23919/ACC.2018.8431892
M3 - Conference contribution
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 728
EP - 734
BT - 2018 Annual American Control Conference, ACC 2018
T2 - 2018 Annual American Control Conference (ACC)
Y2 - 27 June 2018 through 29 June 2018
ER -